Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework-Reference-Cited by-同舟云学术

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Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework

Published:2024-09-03 Issue: Volume: Page:1-25
ISSN:2472-5854
Container-title:IISE Transactions
language:en
Short-container-title:IISE Transactions

Author:

Li Anyi¹^ORCID,Poudel Arun²³,Shao Shuai²³,Shamsaei Nima²³,Liu Jia¹²

Affiliation:

1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA

2. National Center for Additive Manufacturing Excellence (NCAME), Auburn University, Auburn, AL 36849, USA

3. Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725854.2024.2397383

Reference54 articles.

1. Bao H. Wu S. Wu Z. Kang G. Peng X. and Withers P. J. (2021). A machine-learning fatigue life prediction approach of additively manufactured metals. Engineering Fracture Mechanics 242:107508.

2. Caton M. Jones J. W. Boileau J. and Allison J. (1999). The effect of solidification rate on the growth of small fatigue cracks in a cast 319-type aluminum alloy. Metallurgical and Materials Transactions A 30:3055–3068.

3. Choo H. Sham K.-L. Bohling J. Ngo A. Xiao X. Ren Y. Depond P. J. Matthews M. J. and Garlea E. (2019). Effect of laser power on defect texture and microstructure of a laser powder bed fusion processed 316l stainless steel. Materials & Design 164:107534.

4. Criales L. E. Ar isoy Y. M. Lane B. Moylan S. Donmez A. and Özel T. (2017). Laser powder bed fusion of nickel alloy 625: Experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. International Journal of Machine Tools and Manufacture 121:22–36.

5. Dong S. Xiao J. Hu X. Fang N. Liu L. and Yao J. (2023). Deep transfer learning based on bi-lstm and attention for remaining useful life prediction of rolling bearing. Reliability Engineering & System Safety 230:108914.

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